library(ggplot2)
library(dplyr)
Loading data
#BiocManager::install("Hiiragi2013")
library(Hiiragi2013)
library(Biobase)
data(x)
expression <- Biobase::exprs(x)
dftx <- data.frame(pData(x), t(expression))
head(pData(x))
## File.name Embryonic.day Total.number.of.cells lineage genotype
## 1 E3.25 1_C32_IN E3.25 32 WT
## 2 E3.25 2_C32_IN E3.25 32 WT
## 3 E3.25 3_C32_IN E3.25 32 WT
## 4 E3.25 4_C32_IN E3.25 32 WT
## 5 E3.25 5_C32_IN E3.25 32 WT
## 6 E3.25 6_C32_IN E3.25 32 WT
## ScanDate sampleGroup sampleColour
## 1 E3.25 2011-03-16 E3.25 #CAB2D6
## 2 E3.25 2011-03-16 E3.25 #CAB2D6
## 3 E3.25 2011-03-16 E3.25 #CAB2D6
## 4 E3.25 2011-03-16 E3.25 #CAB2D6
## 5 E3.25 2011-03-16 E3.25 #CAB2D6
## 6 E3.25 2011-03-16 E3.25 #CAB2D6
dim(expression)
## [1] 45101 101
groupSize <- table(dftx$sampleGroup)
groupSize
##
## E3.25 E3.25 (FGF4-KO) E3.5 (EPI) E3.5 (FGF4-KO)
## 36 17 11 8
## E3.5 (PE) E4.5 (EPI) E4.5 (FGF4-KO) E4.5 (PE)
## 11 4 10 4
ggplot( dftx, aes( x = X1426642_at, y = X1418765_at )) +
geom_point( aes( color = sampleGroup), shape = 19 ) +
geom_smooth( method = "loess" ) +
scale_color_discrete( guide = FALSE )
Plotly
#install.packages("plotly")
library(plotly)
plt <- ggplot( dftx, aes( x = X1426642_at, y = X1418765_at )) +
geom_point( aes( color = sampleGroup), shape = 19 ) +
geom_smooth( method = "loess" ) +
scale_color_discrete( guide = FALSE )
ggplotly(plt)
plot_ly(dftx, x = ~X1426642_at, y = ~X1418765_at, z = ~X1416967_at,
color = ~Embryonic.day, type = "scatter3d", mode = "markers",
marker = list(size = 4))
Tydying data
selectedProbes <- c(Fgf4 = "X1420085_at", Gata4 = "X1418863_at",
Gata6 = "X1425463_at", Sox2 = "X1416967_at")
genes_expression <- as.matrix(dftx[, selectedProbes])
head(genes_expression)
## X1420085_at X1418863_at X1425463_at X1416967_at
## 1 E3.25 3.027715 4.843137 5.500618 1.731217
## 2 E3.25 9.293016 5.530016 6.160900 9.697038
## 3 E3.25 2.940142 4.418059 4.584961 4.161240
## 4 E3.25 9.715243 5.982314 4.753439 9.540123
## 5 E3.25 8.924228 4.923580 4.629728 8.705340
## 6 E3.25 11.325952 4.068520 4.165692 8.696228
library("reshape2")
genes = melt(genes_expression, varnames = c("sample", "probe"))
head(genes)
genes$gene =
names(selectedProbes)[match(genes$probe, selectedProbes)]
head(genes)
## sample probe value gene
## 1 1 E3.25 X1420085_at 3.027715 Fgf4
## 2 2 E3.25 X1420085_at 9.293016 Fgf4
## 3 3 E3.25 X1420085_at 2.940142 Fgf4
## 4 4 E3.25 X1420085_at 9.715243 Fgf4
## 5 5 E3.25 X1420085_at 8.924228 Fgf4
## 6 6 E3.25 X1420085_at 11.325952 Fgf4
wide <- dcast(genes, formula = sample ~ probe, value.var = "value")
head(wide)
## sample X1420085_at X1418863_at X1425463_at X1416967_at
## 1 1 E3.25 3.027715 4.843137 5.500618 1.731217
## 2 2 E3.25 9.293016 5.530016 6.160900 9.697038
## 3 3 E3.25 2.940142 4.418059 4.584961 4.161240
## 4 4 E3.25 9.715243 5.982314 4.753439 9.540123
## 5 5 E3.25 8.924228 4.923580 4.629728 8.705340
## 6 6 E3.25 11.325952 4.068520 4.165692 8.696228
head(genes)
## sample probe value gene
## 1 1 E3.25 X1420085_at 3.027715 Fgf4
## 2 2 E3.25 X1420085_at 9.293016 Fgf4
## 3 3 E3.25 X1420085_at 2.940142 Fgf4
## 4 4 E3.25 X1420085_at 9.715243 Fgf4
## 5 5 E3.25 X1420085_at 8.924228 Fgf4
## 6 6 E3.25 X1420085_at 11.325952 Fgf4
1D data plots
p = ggplot(genes, aes( x = gene, y = value))
p + geom_boxplot(aes(fill = gene))
p + geom_boxplot() +
geom_jitter(aes(color = gene), width = 0.1, height = 0)
p = ggplot(genes, aes( x = gene, y = value))
p + geom_violin(aes(fill = gene))
p + geom_violin() +
geom_jitter(aes(color = gene), width = 0.1, height = 0)
p = ggplot(genes %>% filter(gene %in% c("Gata4", "Sox2")),
aes(x = value))
p + geom_histogram(aes(fill = gene),
color = "white", bins = 40)
p + geom_histogram(
aes(fill = gene), color="white", alpha=0.6,
bins = 40, position = "identity")
p = ggplot(genes, aes( x = value, color = gene))
p + geom_density()
p = ggplot(genes, aes( x = value, fill = gene))
p + geom_density(alpha = 0.3)
dfx = as.data.frame(Biobase::exprs(x))
ggplot(dfx, aes(x = `64 E4.5 (EPI)`)) + geom_histogram(bins = 100)
ggplot(dfx, aes(x = exp(`64 E4.5 (EPI)`))) + geom_histogram(binwidth = 20) +
xlim(0, 1500)
## Warning: Removed 7302 rows containing non-finite values (stat_bin).
## Warning: Removed 2 rows containing missing values (geom_bar).
simdata = rnorm(70)
simdf <- data.frame(index = seq(along = simdata), sx = sort(simdata))
ggplot(simdf, aes(x = sx, y = index)) + geom_step()
change theme
g = ggplot(iris,
aes(x = Species,
y = Sepal.Length,
fill = Species))+
geom_violin(col = NA)
g
g + theme_minimal()
g + theme_dark()
library(ggthemes)
g + theme_economist_white()
3-5D data
head(mtcars)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
ggplot(data = mtcars) +
geom_point(
aes(x = wt, y = mpg,
shape = factor(gear),
color = factor(cyl),
size = qsec))
Title page
# Title page
library(ggplot2)
library(viridis)
library(season)
library(gridExtra)
pd<-ggplot(schz, aes(year, month, fill = SczBroad)) +
geom_tile(colour="gray20", size=1.5, stat="identity") +
scale_fill_viridis(option="D") +
scale_y_continuous(breaks=1:12, labels=month.abb[1:12])+
xlab("") +
ylab("") +
ggtitle("Total Australian Schizophrenics Born By Month and Year") +
theme(
plot.title = element_text(color="white",hjust=0,vjust=1, size=rel(2)),
plot.background = element_rect(fill="gray20"),
panel.background = element_rect(fill="gray20"),
panel.border = element_rect(fill=NA,color="gray20", size=0.5, linetype="solid"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
axis.text = element_text(color="white", size=rel(1.5)),
axis.text.y = element_text(hjust=1),
legend.text = element_text(color="white", size=rel(1.3)),
legend.background = element_rect(fill="gray20"),
legend.position = "bottom",
legend.title=element_blank()
)
pd
